Middle layers (20-80%) remain stable during SFT while final layers are sensitive, enabling Mid-Block Efficient Tuning that outperforms LoRA by up to 10.2% on GSM8K with reduced parameter count.
InThe Thirteenth In- ternational Conference on Learning Representations
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Layer-wise Analysis of Supervised Fine-Tuning
Middle layers (20-80%) remain stable during SFT while final layers are sensitive, enabling Mid-Block Efficient Tuning that outperforms LoRA by up to 10.2% on GSM8K with reduced parameter count.